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Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.
Produkteigenschaften
- Artikelnummer: 9783540225720
- Medium: Buch
- ISBN: 978-3-540-22572-0
- Verlag: Springer Berlin Heidelberg
- Erscheinungstermin: 25.08.2004
- Sprache(n): Englisch
- Auflage: 2004
- Serie: Lecture Notes in Mathematics
- Produktform: Kartoniert
- Gewicht: 446 g
- Seiten: 284
- Format (B x H x T): 155 x 235 x 16 mm
- Ausgabetyp: Kein, Unbekannt
Themen
- Mathematik | Informatik
- Mathematik
- Numerik und Wissenschaftliches Rechnen
- Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik
- EDV | Informatik
- Informatik
- Künstliche Intelligenz
- Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik
- EDV | Informatik
- Informatik
- Künstliche Intelligenz
- Wissensbasierte Systeme, Expertensysteme